Electrical impedance spectroscopy (EIS) is a tool for characterizing the electrical behavior of matter. Nevertheless, most of the work is focused on purely experimental results, leading aside alternative measurement and estimation techniques. In this paper, we introduce a framework for spectral measurements and parameter estimation applied to EIS. There are two methods in the proposal running independently: frequency response function based non-parametric estimation, and parametric recursive estimation. The former provides consistent estimates even in the presence of noise and works with batches of data. Whilst the latter gives consistent parametric estimates under the right model structure. The proposed platform is designed around a reconfigurable device, which comprises minimal hardware design and digital signal processing. We test the system with a multisine signal by measuring calibration circuits and colloidal samples at microscale. Results show that this method outperforms the state-of-the-art techniques for impedance measurement applications, exhibiting low uncertainty and physical interpretation.
- Impedance spectroscopy
- Frequency response function
- Autoregressive model
- Recursive parameter estimation